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Modeling In-Network Processing and Aggregation in Sensor Networks

Modeling In-Network Processing and Aggregation in Sensor Networks. Ajay Mahimkar EE 382C Embedded Software Systems Prof. B. L. Evans May 5, 2004. Sensor Networks. Monitor physical environment from remote locations Challenges Battery is the most pressing Deployment of sensors in thousands

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Modeling In-Network Processing and Aggregation in Sensor Networks

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  1. Modeling In-Network Processing and Aggregation in Sensor Networks Ajay Mahimkar EE 382C Embedded Software Systems Prof. B. L. Evans May 5, 2004

  2. Sensor Networks • Monitor physical environment from remote locations • Challenges • Battery is the most pressing • Deployment of sensors in thousands • No manual intervention • Design protocols that extend network lifetime • Network lifetime is the time at which first node dies

  3. In-Network Processing • Why data aggregation??? • Individual sensor readings are of limited use • Delivering large amount of data from all nodes to a central point consumes lot of energy • Conserves limited energy and bandwidth • Increases system lifetime

  4. Existing Approaches • Directed Diffusion [Intanagonwiwat, 2003] • LEACH (Low Energy Adaptive Clustering Hierarchy) [Heinzelman, 2000] • Cluster-Head responsible for data aggregation

  5. Existing Approaches … cont. • PEDAP (Power Efficient Data gathering and Aggregation Protocol) [Tan, 2003] • MST (Minimum Spanning Tree) based routing using energy as the metric • Disadvantages • Locally optimizes energy • Increases end-to-end latency

  6. 4 5 A B C 6 3 PEDAP D S DEEPADS 3 7 E F G H 2 1 2 DEEPADS – A Novel Approach • Distributed Energy-Efficient Protocol for Aggregation of Data in Sensor Networks (DEEPADS) • Novel approach that globally maximizes the energy and increases system lifetime

  7. C-DEEPADS • Uses Clustering Approach • Two Tier Methodology • Sensors organize themselves into clusters, each cluster represented by a cluster-head • Global energy metric similar to DEEDAP • Cluster-head aggregates data and transmits to the base station • Reduces end-to-end latency

  8. Simulation • Using Ptolemy-II, VisualSense and Java • Discrete Event Model • Network Simulation • Setup • Environment • 100 m x 100 m area • Sensors location • Uniformly distributed x and y random variables Simulation Parameters

  9. Sensor Node Model

  10. Performance Evaluation

  11. Performance Evaluation … cont.

  12. Discussion • Results • DEEPADS & C-DEEPADS perform much better than existing approaches • Increase in the system lifetime • Reduction in the total energy consumption • Future Work • Repeat experiments taking into consideration the sleep mode in sensors

  13. Questions ???Comments !!!

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